Pre- and Intraoperative Visualization of GRPR-Expressing Solid Tumors: Preclinical Profiling of Novel Dual-Modality Probes for Nuclear and Fluorescence Imaging
Bibliographic record
Abstract
Image-guided surgery using a gastrin-releasing peptide receptor (GRPR)-targeting dual-modality probe could improve the accuracy of the resection of various solid tumors. The aim of this study was to further characterize our four previously developed GRPR-targeting dual-modality probes that vary in linker structures and were labeled with indium-111 and sulfo-cyanine 5. Cell uptake studies with GRPR-positive PC-3 cells and GRPR-negative NCI-H69 cells confirmed receptor specificity. Imaging and biodistribution studies at 4 and 24 h with 20 MBq/1 nmol [111In]In-12-15 were performed in nude mice bearing a PC-3 and NCI-H69 xenograft, and showed that the probe with only a pADA linker in the backbone had the highest tumor-to-organ ratios (T/O) at 24 h after injection (T/O > 5 for, e.g., prostate, muscle and blood). For this probe, a dose optimization study with three doses (0.75, 1.25 and 1.75 nmol; 20 MBq) revealed that the maximum image contrast was achieved with the lowest dose. Subsequently, the probe was successfully used for tumor excision in a simulated image-guided surgery setting. Moreover, it demonstrated binding to tissue sections of human prostate, breast and gastro-intestinal stromal tumors. In summary, our findings demonstrate that the developed dual-modality probe has the potential to aid in the complete surgical removal of GRPR-positive tumors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".